1. Field of the Invention
The present invention relates to electrocardiogram (ECG) identification and/or verification technologies, in particulate relates to a method for calibrating and normalizing ECG signals used in ECG identification systems for enhancing the identification rate under various heart rates.
2. Description of Prior Art
In recent years, the biometrics technologies are applied in many commercial products, for example, the fingerprint identification and the iris identification are widely utilized in daily life. Biometrics technologies are essential for user identification and gradually improve other traditional security means such as ID cards, passwords, and keys because they have delivered the security mechanisms which are more convenient and secure.
Though, the mentioned fingerprint and iris identification methods are applied in current commercial products. However, the researches indicate some concerning facts on unauthorized duplicates or counterfeits with current biometric systems. For example, users may leave their fingerprints on a touched surface. Therefore, a third party may create a fingerprint duplicate based on that surface imprint. Further, if a third party gives an iris identification sample wearing specialized lens and duplicates the lens to other people, anyone wearing the lens pass the comparison because of the specialized lens.
Recent researches also shows, in addition to the known arts such as fingerprints and iris bio characteristics, the human ECGs are different from person to person and qualify to utilize in identification. FIG. 1 is a schematic diagram of the ECG signal monitoring system according to the prior art. A user 1 measures the ECG of oneself by an ECG monitoring device 2, and the ECG monitoring device 2 records a series of ECG 3. A normal ECG 3 from each individual typically has characteristic points P, Q, R, S, T, yet the relative positions vary, whereby the comparison of the characteristic points are used for determining authorization of the user 1.
However, human heart rates may influence by emotions (such as excitements, tensions, and pressures), postures (such as standing, sitting, and lying down), and activity levels. The morphology of ECG 3 (differences of the width and the height of one beat waveform) generated by the same person is various from time to time because of changes of heart rates. Thus, it is difficult to obtain consistant ECG measurements for each identification process. For example, when an ECG identification system is used in an access control device and an ECG template of the user 1 is measured and recorded in a resting state for identification usages in the future. When the user 1 tries to pass the identification authorization after doing exercise, the heart rate can be much higher than the resting state, the user then has difficulty to pass the security check under the circumstance.
There exists several ECG identification methods are suggested in the academic community, such as the time domain analysis, the frequency domain analysis, the chaos analysis and so on. The previous methods attempt to improve the ECG identification rates and to ignore the interference caused from various heart rates. However, regardless of the identification methods applied, the user 1 has to be in the same state as the state when the template made in order to provide higher identification rates. The current technologies remain unsolved to identify the same person having various heart rates. Hence, because of above reasons, the ECG identification technologies are still on the research stages and not ready to be applied in the market.